Have you ever used a Roomba vacuum? Or told Alexa to play your favorite song? Surely you have seen many product advertisements while scrolling on social media. You might not have realized it at the time, but you were experiencing machine learning.
A branch of artificial intelligence, machine learning is the ability of computer systems to learn from experiences without being programmed.
If you’re not familiar with machine learning, don’t worry. There are many courses you can take to fill the gaps. Discover everything you need to know about machine learning by taking the following courses aimed at all levels of learners.
What Is Machine Learning?
So, what exactly is machine learning? Many of us use it regularly in our daily lives when we make requests of Siri or Alexa. Machine learning is a type of AI in which computers use data to learn how to do tasks, instead of being programmed to do them. This makes it possible for machines to become smarter over time as they encounter new data. Data science and machine learning go hand-in-hand, but it is a vast field.
The process of machine learning involves preparing a training data set, choosing an algorithm, training the algorithm to create a model, and using the model to improve it.
A machine learning algorithm can be categorized as either supervised or unsupervised learning. Supervised algorithms are when past data can be applied to new data to predict future events. Unsupervised machine learning algorithms are used to explore data and draw inferences from datasets.
There are also several other different types of machine learning algorithms, including linear regression, logistic regression, decision tree, and random forest.
A method of data analysis, machine learning uses information to design a model and learn trends. According to Microsoft, Machine learning models are “a file that has been trained to recognize certain types of patterns.” Training a model to review a set of data through an algorithm will allow it to learn from that data.
Deep learning is an advanced aspect of machine learning. Although some of the courses below might explore deep learning, it is typically reserved for advanced students.
Criteria for a Great Machine Learning Course
Although most of these machine learning courses are free, it’s still important to make sure a course is worth the time investment. When perusing the online courses available, consider the criteria below.
Online machine learning courses should be focused, meaning that machine learning should be the course’s main subject. Additionally, they should involve the use of a programming language that is free and open-sourced, like Octave, Python, or R.
For an online machine learning course to be useful, it should also be interactive. The professor should lead you in an engaging lecture instead of just assigning readings. The professor should also assign hands-on projects that have real-world applications.
Practical courses should be readily available according to your schedule and should be updated frequently. Additionally, make sure to check the reviews of a course to get a first-hand idea of what the course will be like.
The Best Online Machine Learning Courses
The following machine learning courses meet all of the criteria that makes for an excellent course. Along with being focused, interactive, and readily available, they also feature professors who are experts in their field.
Some of the courses below are also offered in conjunction with Ivy League schools such as Stanford and Columbia. Let’s explore the best online machine learning courses.
Price: Free, or $79 including a certificate
Time Commitment: Approximately 60 hours
This machine learning course by Stanford is an excellent introductory course and is mirrored by many other courses in the field. The course is taught by the co-founder of Coursera himself, Professor Andrew Ng, who is also the founding lead of Google Brain and a former chief scientist at Baidu.
This course, which sparked the creation of Coursera, covers a broad range of information on machine learning.
Instead of using the programming languages Python or R, the course uses Octave. While this may be a deterrent to some, Octave is a great language to study for machine learning.
Keep in mind that the course also covers calculus and a refresher in linear algebra. After completing this course, you can move on to more advanced studies such as deep learning and engineering.
Price: Free, or $249 including a certificate
Time Commitment: 12 weeks
A level up from the Stanford course, Columbia offers this advanced version through edX. The course is taught by Columbia machine learning professor Dr John W Paisley. Among the topics covered in the 12-week program are classification and regression, clustering methods, sequential models, and topic modeling.
To enroll, students should already understand calculus, linear algebra, probability, coding, and statistics.
Price: Free, certificate available
Time Commitment: 30 weeks
Study machine learning using case studies in this Coursera class hosted by the University of Washington. Taught by professors Emily Fox and Carlos Guestrin, you will cover significant areas of the topic, including classification, clustering, and information retrieval.
In just three hours a week for approximately seven months, you will learn how to analyze large and complex data sets. Earn a certificate upon completion for a small fee.
Level: Beginner – Intermediate
Time Commitment: 44 hours
Designed by professional data scientists, the Machine Learning A-Z course on Udemy will help you learn theories, algorithms, and how to use coding libraries. The course is available to a wide variety of people, including college students and professionals. The only prerequisite to enroll is an understanding of high school mathematics.
The course covers data processing, deep learning, artificial neural networks and convolutional neural networks. Although the course is not free, it includes a lifetime access to the material.
Price: Free, or $49 for a certificate
Time Commitment: Approximately 10 months
Provided by the National Research University’s Higher School of Economics, this advanced machine learning specialization course via Coursera features a broad range of techniques.
Taught by the lead data scientist at Yandex, Evgeny Sokolov, the course features hands-on experience, including how to apply machine learning techniques to real-world artificial intelligence. This flexible class is aimed at those already in the industry.
Time Commitment: 1 hour 20 minutes, twice per week
Prestigious Carnegie Mellon professor Larry Wasserman teaches statistical machine learning, one of the most advanced courses on this list. A regression course and intermediate statistics course are prerequisites for this class.
Students of this course will cover statistical theories crucial to machine learning, including nonparametric theory, consistency, minimax estimation, and concentration of measure. The online course is self-paced, but it is also possible to attend the same classes at the Carnegie Mellon campus.
Time Commitment: 16 weeks
This course is the free online version of an actual graduate-level class offered at Georgia Tech. It is recommended that you take a course in AI before enrolling in this one. If you aren’t sure whether you are ready for this course, browse the course preparedness questions.
The course is a part of the Online Master of Science in Computer Science at Georgia Tech. If you want to commit to taking the full degree program online, it will cost you $7,000.
Is a Machine Learning Course Right For You?
If you are interested in machine learning, you will certainly be able to find a class that is right for you. Pursuing a degree in computer science is an option, but it can be pricey.
Taking an individual machine learning course is a great place to start instead, since many of them are free and accredited. Additionally, many are hosted by Ivy League institutions that offer impressive curriculums.
Even if you are at the advanced level of machine learning, you will be able to find plenty of challenging courses online. All it takes to succeed is an interest in machine learning and the motivation to complete the class.